Night Time Vehicle Detection Using Machine Learning
Abstract: Monitoring traffic using camera networks is crucial, especially at night when visibility decreases and accident risks rise. Existing methods often struggle with the erratic nature of vehicle lights in low - light conditions, where lights appear as flashes or complex patterns across disconnected image regions. This study introduces a real - time vehicle detection algorithm designed for night time scenarios, leveraging machine learning with a grid of foveal classifiers. These classifiers use a single global image descriptor to predict vehicle locations based on their positions within the grid and ground - truth data. By requiring only point - based annotations for training, the algorithm accelerates database creation. Experimental validation on a new nighttime dataset demonstrates the effectiveness of this approach in accurately detecting vehicles under challenging lighting conditions.
Keywords: Databases, Vehicle detection, Real - time systems, Lighting, Annotations, Cameras
How to Cite?: Syeda Aamina Khadri, Dr. S. Ramacharan, "Night Time Vehicle Detection Using Machine Learning", Volume 13 Issue 8, August 2024, International Journal of Science and Research (IJSR), Pages: 1160-1164, https://www.ijsr.net/getabstract.php?paperid=SR24817100208, DOI: https://dx.doi.org/10.21275/SR24817100208